Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
10-2024
Abstract
Deep Learning Systems (DLSs) have been widely applied in safety-critical tasks such as autopilot. However, when a perturbed input is fed into a DLS for inference, the DLS often has incorrect outputs (i.e., faults). DLS testing techniques (e.g., DeepXplore) detect such faults by generating perturbed inputs to explore data flows that induce faults. Since a DLS often has infinitely many data flows, existing techniques require developers to manually specify a set of activation values in a DLS’s neurons for exploring fault-inducing data flows. Unfortunately, recent studies show that such manual effort is tedious and can detect only a tiny proportion of fault-inducing data flows. In this paper, we present Themis, the first automatic DLS testing system, which attains strong fault detection capability by ensuring a full coverage of fault-inducing data flows at a high probability. Themis carries a new workflow for automatically and systematically revealing data flows whose internal neurons’ outputs vary substantially when the inputs are slightly perturbed, as these data flows are likely fault-inducing. We evaluated Themis on ten different DLSs and found that on average the number of faults detected by Themis was 3.78X more than four notable DLS testing techniques. By retraining all evaluated DLSs with the detected faults, Themis also increased (regained) these DLSs’ accuracies on average 14.7X higher than all baselines.
Keywords
Deep Learning Systems (DLSs), Safety-critical tasks, Autopilot, Fault detection, Perturbed input, DLS testing techniques, Data flows
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Data Science and Engineering; Information Systems and Management
Publication
Proceeding of the 35th International Symposium on Software Reliability Engineering, Tsukuba, Japan, 2024 October 28-31
Identifier
10.48550/arXiv.2405.09314
Publisher
IEEE
City or Country
New Jersey
Citation
HUANG, Dong; LI, Tsz On; XIE, Xiaofei; and CUI, Heming.
Themis: Automatic and efficient deep learning system testing with strong fault detection capability. (2024). Proceeding of the 35th International Symposium on Software Reliability Engineering, Tsukuba, Japan, 2024 October 28-31.
Available at: https://ink.library.smu.edu.sg/sis_research/9509
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.48550/arXiv.2405.09314